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題名 從高維度消費紀錄挖掘隱藏偏好
Discovering Hidden Preferences from High Dimensional Consumption Records作者 陳品嘉
Chen, Pin-Chia貢獻者 莊皓鈞<br>林靖庭
Chuang, Hao-Chun<br>Lin, Ching-Ting
陳品嘉
Chen, Pin-Chia關鍵詞 高維度資料
主題模型
非負矩陣分解
深度學習
High-dimension data
Topic modeling
NMF
Deep learning日期 2023 上傳時間 1-Sep-2023 14:47:45 (UTC+8) 摘要 理解消費者行為在許多領域中都被認為是重要的信息，尤其是在 市場營銷中。但是，複雜的行為以及高維度、動態數據使得從中提取 有意義的洞察變得困難。為了解決這些問題，我們結合了非負矩陣分 解 (NMF) 和遞迴神經網絡 (RNN)，提出深度動態神經網路 (Dynamic Deep NMF)，來捕捉動態模式。NMF 的分解幫助我們總結消費主題 和用戶對主題的興趣。而 RNN 的遞迴特性則幫助我們捕捉消費者 的動態模式。我們設計了一個模擬實驗，產生模擬數據以測試 NMF 和 Dynamic Deep NMF 的性能。最後，我們使用一個實證數據來展示 Dynamic Deep NMF 會找到什麼隱藏主題，以及它如何捕捉動態用戶 行為。
To understand users’ consumption behavior is found critical in many fields, especially marketing. But the complex behavior embedded in high-dimensional, dynamic transaction data make it hard to extract meaningful insights. To tackle such problems, we combine the non-negative matrix factorization(NMF) and recurrent neural network (RNN) to develop a Dynamic Deep NMF in order to capture dynamic patterns and elicit hidden preferences. The decomposition of NMF helps us to summarize the consumption topics and users’ interests among the topics. And the recurrent properties of RNN helps us to capture the dynamic pattern of users’ interests. We also develop a simulation experiment to generate synthetic data to test the performances of NMF and Dynamic Deep NMF. Finally, we use an empirical dataset to demonstrate what hidden topics the Dynamic Deep NMF could find and how the method captures dynamic user behaviors.參考文獻 參考文獻 [1] A. V. Bodapati, “Recommendation systems with purchase data,” Journal of Marketing Research, vol. 45, no. 1, pp. 77–93, 2008. [Online]. Available: https://doi.org/10.1509/jmkr.45.1.077 [2] J. R. Hauser, G. L. Urban, G. Liberali, and M. Braun, “Website morphing,” Marketing Science, vol. 28, no. 2, pp. 202–223, 2009. [Online]. Available: http://www.jstor.org/stable/23884254 [3] J. R. Hauser, G. G. Liberali, and G. L. Urban, “Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph,” Management Science, vol. 60, no. 6, pp. 1594–1616, 2014. [Online]. Available: https: //doi.org/10.1287/mnsc.2014.1961 [4] A. Goldfarb and C. Tucker, “Online display advertising: Targeting and obtrusiveness,” Marketing Science, vol. 30, no. 3, pp. 389–404, 2011. [Online]. Available: http://www.jstor.org/stable/23012474 [5] C. Perlich, B. Dalessandro, T. Raeder, O. Stitelman, and F. Provost, “Machine learning for targeted display advertising: Transfer learning in action,” Mach. Learn., vol. 95, no. 1, p. 103–127, apr 2014. [Online]. Available: https: //doi.org/10.1007/s10994-013-5375-2 [6] D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, pp. 788–791, 1999. 25 [7] J. K. Pritchard, M. Stephens, and P. Donnelly, “Inference of Population Structure Using Multilocus Genotype Data,” Genetics, vol. 155, no. 2, pp. 945–959, 06 2000. [Online]. Available: https://doi.org/10.1093/genetics/155.2.945 [8] D. Guillamet and J. Vitrià, “Non-negative matrix factorization for face recognition,” in Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence, ser. CCIA ’02. Berlin, Heidelberg: Springer-Verlag, 2002, p. 336–344. [9] D. Lee and H. Seung, “Algorithms for non-negative matrix factorization,” in Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000, ser. Advances in Neural Information Processing Systems. Neural information processing systems foundation, 2001, 14th Annual Neural Information Processing Systems Conference, NIPS 2000 ; Conference date: 27-11-2000 Through 02-12-2000. [10] W. Xu, X. Liu, and Y. Gong, “Document clustering based on non-negative matrix factorization,” in Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, ser. SIGIR ’03. New York, NY, USA: Association for Computing Machinery, 2003, p. 267–273. [Online]. Available: https://doi.org/10.1145/860435.860485 [11] J. Mejia, S. Mankad, and A. Gopal, “Service quality using text mining: Measurement and consequences,” Manufacturing & Service Operations Management, vol. 23, no. 6, p. 1354–1372, nov 2021. [Online]. Available: https://doi.org/10.1287/msom. 2020.0883 [12] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1127647 [13] F. Ye, C. Chen, and Z. Zheng, “Deep autoencoder-like nonnegative matrix factorization for community detection,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ser. CIKM 26 ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 1393– 1402. [Online]. Available: https://doi.org/10.1145/3269206.3271697 [14] J. Wang and X.-L. Zhang, “Deep nmf topic modeling,” Neurocomput., vol. 515, no. C, p. 157–173, jan 2023. [Online]. Available: https://doi.org/10.1016/j.neucom. 2022.10.002 [15] P. S. Dhillon and S. Aral, “Modeling dynamic user interests: A neural matrix factorization approach,” Marketing Science, vol. 40, no. 6, p. 1059–1080, nov 2021. [Online]. Available: https://doi.org/10.1287/mksc.2021.1293 [16] T. G. Kang, K. Kwon, J. W. Shin, and N. S. Kim, “Nmf-based target source separation using deep neural network,” IEEE Signal Processing Letters, vol. 22, no. 2, pp. 229– 233, 2015. [17] J. Le Roux, J. R. Hershey, and F. Weninger, “Deep nmf for speech separation,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp. 66–70. [18] C. Févotte and J. Idier, “Algorithms for nonnegative matrix factorization with the β-divergence,” Neural Computation, vol. 23, no. 9, pp. 2421–2456, 2011. [19] A. CICHOCKI and A.-H. PHAN, “Fast local algorithms for large scale nonnegative matrix and tensor factorizations,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E92.A, no. 3, pp. 708–721, 2009. [20] J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 1532–1543. [Online]. Available: https://aclanthology.org/D14-1162 [21] X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research, G. Gordon, 27 D. Dunson, and M. Dudík, Eds., vol. 15. Fort Lauderdale, FL, USA: PMLR, 11–13 Apr 2011, pp. 315–323. [Online]. Available: https://proceedings.mlr.press/ v15/glorot11a.html [22] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. [23] W. Webber, A. Moffat, and J. Zobel, “A similarity measure for indefinite rankings,” ACM Trans. Inf. Syst., vol. 28, no. 4, nov 2010. [Online]. Available: https://doi.org/10.1145/1852102.1852106 [24] M. Kohjima, T. Matsubayashi, and H. Sawada, “Non-negative multiple matrix factorization for consumer behavior pattern extraction by considering attribution information,” Transactions of the Japanese Society for Artificial Intelligence, vol. 30, no. 6, pp. 745–754, 2015 描述 碩士
國立政治大學
金融學系
110352019資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110352019 資料類型 thesis dc.contributor.advisor 莊皓鈞<br>林靖庭 zh_TW dc.contributor.advisor Chuang, Hao-Chun<br>Lin, Ching-Ting en_US dc.contributor.author (Authors) 陳品嘉 zh_TW dc.contributor.author (Authors) Chen, Pin-Chia en_US dc.creator (作者) 陳品嘉 zh_TW dc.creator (作者) Chen, Pin-Chia en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Sep-2023 14:47:45 (UTC+8) - dc.date.available 1-Sep-2023 14:47:45 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2023 14:47:45 (UTC+8) - dc.identifier (Other Identifiers) G0110352019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146861 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 110352019 zh_TW dc.description.abstract (摘要) 理解消費者行為在許多領域中都被認為是重要的信息，尤其是在 市場營銷中。但是，複雜的行為以及高維度、動態數據使得從中提取 有意義的洞察變得困難。為了解決這些問題，我們結合了非負矩陣分 解 (NMF) 和遞迴神經網絡 (RNN)，提出深度動態神經網路 (Dynamic Deep NMF)，來捕捉動態模式。NMF 的分解幫助我們總結消費主題 和用戶對主題的興趣。而 RNN 的遞迴特性則幫助我們捕捉消費者 的動態模式。我們設計了一個模擬實驗，產生模擬數據以測試 NMF 和 Dynamic Deep NMF 的性能。最後，我們使用一個實證數據來展示 Dynamic Deep NMF 會找到什麼隱藏主題，以及它如何捕捉動態用戶 行為。 zh_TW dc.description.abstract (摘要) To understand users’ consumption behavior is found critical in many fields, especially marketing. But the complex behavior embedded in high-dimensional, dynamic transaction data make it hard to extract meaningful insights. To tackle such problems, we combine the non-negative matrix factorization(NMF) and recurrent neural network (RNN) to develop a Dynamic Deep NMF in order to capture dynamic patterns and elicit hidden preferences. The decomposition of NMF helps us to summarize the consumption topics and users’ interests among the topics. And the recurrent properties of RNN helps us to capture the dynamic pattern of users’ interests. We also develop a simulation experiment to generate synthetic data to test the performances of NMF and Dynamic Deep NMF. Finally, we use an empirical dataset to demonstrate what hidden topics the Dynamic Deep NMF could find and how the method captures dynamic user behaviors. en_US dc.description.tableofcontents 第 一章 緒論 1 第 二章 文獻回顧 3 2.1 非負矩陣分解 3 2.2 神經網路與非負矩陣分解 4 第 三章 研究方法 6 3.1 非負矩陣分解 6 3.2 深度動態非負整數分解 7 第 四章 模擬實驗 11 4.1 實驗設計 11 4.2 實驗結果 13 第 五章 實證分析 19 5.1 商品品類層級分析 19 5.2 商品品項層級分析 21 第 六章 結論 24 參考文獻 26 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110352019 en_US dc.subject (關鍵詞) 高維度資料 zh_TW dc.subject (關鍵詞) 主題模型 zh_TW dc.subject (關鍵詞) 非負矩陣分解 zh_TW dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) High-dimension data en_US dc.subject (關鍵詞) Topic modeling en_US dc.subject (關鍵詞) NMF en_US dc.subject (關鍵詞) Deep learning en_US dc.title (題名) 從高維度消費紀錄挖掘隱藏偏好 zh_TW dc.title (題名) Discovering Hidden Preferences from High Dimensional Consumption Records en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 參考文獻 [1] A. V. Bodapati, “Recommendation systems with purchase data,” Journal of Marketing Research, vol. 45, no. 1, pp. 77–93, 2008. [Online]. Available: https://doi.org/10.1509/jmkr.45.1.077 [2] J. R. Hauser, G. L. Urban, G. Liberali, and M. Braun, “Website morphing,” Marketing Science, vol. 28, no. 2, pp. 202–223, 2009. [Online]. Available: http://www.jstor.org/stable/23884254 [3] J. R. Hauser, G. G. Liberali, and G. L. Urban, “Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph,” Management Science, vol. 60, no. 6, pp. 1594–1616, 2014. [Online]. Available: https: //doi.org/10.1287/mnsc.2014.1961 [4] A. Goldfarb and C. Tucker, “Online display advertising: Targeting and obtrusiveness,” Marketing Science, vol. 30, no. 3, pp. 389–404, 2011. [Online]. Available: http://www.jstor.org/stable/23012474 [5] C. Perlich, B. Dalessandro, T. Raeder, O. Stitelman, and F. Provost, “Machine learning for targeted display advertising: Transfer learning in action,” Mach. Learn., vol. 95, no. 1, p. 103–127, apr 2014. [Online]. Available: https: //doi.org/10.1007/s10994-013-5375-2 [6] D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, pp. 788–791, 1999. 25 [7] J. K. Pritchard, M. Stephens, and P. Donnelly, “Inference of Population Structure Using Multilocus Genotype Data,” Genetics, vol. 155, no. 2, pp. 945–959, 06 2000. [Online]. Available: https://doi.org/10.1093/genetics/155.2.945 [8] D. Guillamet and J. Vitrià, “Non-negative matrix factorization for face recognition,” in Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence, ser. CCIA ’02. Berlin, Heidelberg: Springer-Verlag, 2002, p. 336–344. [9] D. Lee and H. Seung, “Algorithms for non-negative matrix factorization,” in Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000, ser. Advances in Neural Information Processing Systems. Neural information processing systems foundation, 2001, 14th Annual Neural Information Processing Systems Conference, NIPS 2000 ; Conference date: 27-11-2000 Through 02-12-2000. [10] W. Xu, X. Liu, and Y. Gong, “Document clustering based on non-negative matrix factorization,” in Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, ser. SIGIR ’03. New York, NY, USA: Association for Computing Machinery, 2003, p. 267–273. [Online]. Available: https://doi.org/10.1145/860435.860485 [11] J. Mejia, S. Mankad, and A. Gopal, “Service quality using text mining: Measurement and consequences,” Manufacturing & Service Operations Management, vol. 23, no. 6, p. 1354–1372, nov 2021. [Online]. Available: https://doi.org/10.1287/msom. 2020.0883 [12] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1127647 [13] F. Ye, C. Chen, and Z. Zheng, “Deep autoencoder-like nonnegative matrix factorization for community detection,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ser. CIKM 26 ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 1393– 1402. [Online]. Available: https://doi.org/10.1145/3269206.3271697 [14] J. Wang and X.-L. Zhang, “Deep nmf topic modeling,” Neurocomput., vol. 515, no. C, p. 157–173, jan 2023. [Online]. Available: https://doi.org/10.1016/j.neucom. 2022.10.002 [15] P. S. Dhillon and S. Aral, “Modeling dynamic user interests: A neural matrix factorization approach,” Marketing Science, vol. 40, no. 6, p. 1059–1080, nov 2021. [Online]. Available: https://doi.org/10.1287/mksc.2021.1293 [16] T. G. Kang, K. Kwon, J. W. Shin, and N. S. Kim, “Nmf-based target source separation using deep neural network,” IEEE Signal Processing Letters, vol. 22, no. 2, pp. 229– 233, 2015. [17] J. Le Roux, J. R. Hershey, and F. Weninger, “Deep nmf for speech separation,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp. 66–70. [18] C. Févotte and J. Idier, “Algorithms for nonnegative matrix factorization with the β-divergence,” Neural Computation, vol. 23, no. 9, pp. 2421–2456, 2011. [19] A. CICHOCKI and A.-H. PHAN, “Fast local algorithms for large scale nonnegative matrix and tensor factorizations,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E92.A, no. 3, pp. 708–721, 2009. [20] J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 1532–1543. [Online]. Available: https://aclanthology.org/D14-1162 [21] X. Glorot, A. Bordes, and Y. Bengio, “Deep sparse rectifier neural networks,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research, G. Gordon, 27 D. Dunson, and M. Dudík, Eds., vol. 15. Fort Lauderdale, FL, USA: PMLR, 11–13 Apr 2011, pp. 315–323. [Online]. Available: https://proceedings.mlr.press/ v15/glorot11a.html [22] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. [23] W. Webber, A. Moffat, and J. Zobel, “A similarity measure for indefinite rankings,” ACM Trans. Inf. Syst., vol. 28, no. 4, nov 2010. [Online]. Available: https://doi.org/10.1145/1852102.1852106 [24] M. Kohjima, T. Matsubayashi, and H. Sawada, “Non-negative multiple matrix factorization for consumer behavior pattern extraction by considering attribution information,” Transactions of the Japanese Society for Artificial Intelligence, vol. 30, no. 6, pp. 745–754, 2015 zh_TW
